import os
import sys
sys.path.append(os.getcwd())
from openrec import ImplicitModelTrainer
from openrec.utils import ImplicitDataset
from openrec.recommenders import WCML
from openrec.utils.evaluators import AUC
from openrec.utils.samplers import NPairwiseSampler
import dataloader
raw_data = dataloader.load_citeulike()
batch_size = 2000
test_batch_size = 100
display_itr = 500
train_dataset = ImplicitDataset(raw_data['train_data'], raw_data['max_user'], raw_data['max_item'], name='Train')
val_dataset = ImplicitDataset(raw_data['val_data'], raw_data['max_user'], raw_data['max_item'], name='Val')
test_dataset = ImplicitDataset(raw_data['test_data'], raw_data['max_user'], raw_data['max_item'], name='Test')
model = WCML(batch_size=batch_size, max_user=train_dataset.max_user(), max_item=train_dataset.max_item(),
dim_embed=20, neg_num=5, l2_reg=None, opt='Adam', sess_config=None)
sampler = NPairwiseSampler(batch_size=batch_size, dataset=train_dataset, negativenum=5, num_process=5)
model_trainer = ImplicitModelTrainer(batch_size=batch_size, test_batch_size=test_batch_size,
train_dataset=train_dataset, model=model, sampler=sampler)
auc_evaluator = AUC()
model_trainer.train(num_itr=int(1e5), display_itr=display_itr, eval_datasets=[val_dataset],
evaluators=[auc_evaluator], num_negatives=200)
print 'nah'
raw_data = dataloader.load_music()
print 'yah'
batch_size = 1000
test_batch_size = 100
display_itr = 10000
print 'yuck'
train_dataset = ImplicitDataset(raw_data['train_data'], raw_data['max_user'], raw_data['max_item'], name='Train')
print 'h'
val_dataset = ImplicitDataset(raw_data['val_data'], raw_data['max_user'], raw_data['max_item'], name='Val')
print 'e'
test_dataset = ImplicitDataset(raw_data['test_data'], raw_data['max_user'], raw_data['max_item'], name='Test')
print 'here 1'
youtube_model = YoutubeRec(batch_size=batch_size, max_user=train_dataset.max_user(), max_item=train_dataset.max_item(),
dim_embed=20, sess_config=sess_config, opt='Adam')
# bpr_model = FeatureBasedBPR(batch_size=batch_size, max_user=train_dataset.max_user(), max_item=train_dataset.max_item(),
# dim_embed=20, opt='Adam', sess_config=sess_config)
print 'here 2'
sampler = GeneralSampler(batch_size=batch_size, dataset=train_dataset, num_process=1, genre_f = raw_data['song_to_genre'])
print 'here 3'
model_trainer = ImplicitModelTrainer(batch_size=batch_size, test_batch_size=test_batch_size,
train_dataset=train_dataset, model=youtube_model, sampler=sampler)
print 'here 4'
auc_evaluator = AUC()
print 'here 5'
model_trainer.train(num_itr=int(1e6), display_itr=display_itr, eval_datasets=[val_dataset, test_dataset],
evaluators=[auc_evaluator])
display_itr = 10
train_dataset = ImplicitDataset(raw_data['train_data'],
raw_data['max_user'],
raw_data['max_item'],
name='Train')
val_dataset = ImplicitDataset(raw_data['val_data'],
raw_data['max_user'],
raw_data['max_item'],
name='Val')
test_dataset = ImplicitDataset(raw_data['test_data'],
raw_data['max_user'],
raw_data['max_item'],
name='Test')
max_user = train_dataset.max_user()
max_item = train_dataset.max_item()
# bpr_model = BPR(batch_size=batch_size, max_user=train_dataset.max_user(), max_item=train_dataset.max_item(),
# dim_embed=20, opt='Adam', sess_config=sess_config)
# sampler = PairwiseSampler(batch_size=batch_size,
# dataset=train_dataset, num_process=5)
# model_trainer = ImplicitModelTrainer(batch_size=batch_size, test_batch_size=test_batch_size,
# train_dataset=train_dataset, model=bpr_model, sampler=sampler)
# auc_evaluator = AUC()
# model_trainer.train(num_itr=int(10), display_itr=display_itr, eval_datasets=[val_dataset, test_dataset],
# evaluators=[auc_evaluator])
# print("Save")
# bpr_model.save("./model", 1)
import os
import sys
sys.path.append(os.getcwd())
from openrec import ImplicitModelTrainer
from openrec.utils import ImplicitDataset
from openrec.recommenders import BPR
from openrec.utils.evaluators import AUC
from openrec.utils.samplers import PairwiseSampler
from config import sess_config
import dataloader
raw_data = dataloader.load_citeulike()
batch_size = 1000
test_batch_size = 100
display_itr = 10000
train_dataset = ImplicitDataset(raw_data['train_data'], raw_data['max_user'], raw_data['max_item'], name='Train')
val_dataset = ImplicitDataset(raw_data['val_data'], raw_data['max_user'], raw_data['max_item'], name='Val')
test_dataset = ImplicitDataset(raw_data['test_data'], raw_data['max_user'], raw_data['max_item'], name='Test')
bpr_model = BPR(batch_size=batch_size, max_user=train_dataset.max_user(), max_item=train_dataset.max_item(),
dim_embed=20, opt='Adam', sess_config=sess_config)
sampler = PairwiseSampler(batch_size=batch_size, dataset=train_dataset, num_process=1)
model_trainer = ImplicitModelTrainer(batch_size=batch_size, test_batch_size=test_batch_size,
train_dataset=train_dataset, model=bpr_model, sampler=sampler)
auc_evaluator = AUC()
model_trainer.train(num_itr=int(1e6), display_itr=display_itr, eval_datasets=[val_dataset, test_dataset],
evaluators=[auc_evaluator])